Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm and I’m Byron Reese. Today my guest is Ron Green. Ron is the CTO over at KUNGFU.AI. He holds a BA in Computer Science from the University of Texas at Austin, and he holds a Master of Science from the University of Sussex in Evolutionary and Adaptive Systems. His company, KUNGFU.AI is a professional services company that helps companies start and accelerate artificial intelligence projects. I asked him [to be] on the show today because I wanted to do an episode that was a little more ‘hands-on’ about how an enterprise today can apply this technology to their business. Welcome to the show, Ron.

Ron Green: Thank you for having me.

So let’s start with that—what sizes of organizations you see that are starting [to do] machine learning kind of projects?

Yeah, we’re seeing companies really at all sizes and all stages, meaning … really large companies, you’d be surprised, Fortune 500 level companies that may have some data science experience, but really looking to come up to speed and take advantage of a lot of recent advances in machine learning. So we work typically with two of what we call mid-tier companies, [those with] 100 million to maybe 2 billion of revenue, and we’re seeing really pretty much across the spectrum everybody moving into machine learning and AI.

But there has to be a lower end. I don’t think my dry cleaner is spinning up any projects with you?

No, absolutely.

What size of company should not even, I mean, they’ll use tools that have been built using it, but in terms of like… amassing their own data and doing their own development on it, what would be a small company [for which] probably, it doesn’t make sense for this one?

Yeah. Well, I mean at the lowest level, if you’re talking about some guy starting a company on their own, with… the open-source machine learning libraries that are out there, if you are trying to do something to let’s say use natural language processing as a part of your project, there really is no lower bound now. I mean, a couple of guys in a garage could take advantage of these techniques in an affordable way and integrate them into the product. I really don’t think there’s a lower bound.

So let’s walk through the life cycle of a project. I’m an enterprise with—let’s say a development department of programmers that maybe has 200 people. And I get the edict from the CEO that we need to do some of ‘that AI stuff’ that he/she is hearing a lot about. How do you start and identify places that the technology can be applied to?

We really have a methodology for that, and it’s pretty straightforward. You need a combination of a few things. You’ve really got to understand the business and the business objectives. If you just walk in and you start building technology for technology’s sake, you’re not helping anybody, but you’ve also got to marry that with an understanding of what data is available.

So you may have a really high-level important strategic initiative that you want to solve, but you don’t have the data. And we see that occasionally, and in those instances, it’s not the best outcome, but knowing sooner [rather] than later that you need to start collecting data, or you need to start augmenting or brokering your data, that’s a good thing to learn sooner than later. But lastly, you really need to understand what the state of the art is.

So you marry the business objective with the available data and the feasibility. Things are moving pretty quickly in a couple of fields, especially computer vision and natural language processing. So something that wasn’t even possible a couple of years ago may be possible now. And you look at the intersection, and we very much are – we consider ourselves sort of practical AI, in that we’re not interested in taking your two-year research projects.

We’re very much about building solutions today with the tools that are available today and getting them into production. And so we find an intersection of those three areas and try to typically move quickly, you know, have things completed within a quarter and live, so that companies can get a quick win under their belt and get confidence about moving into this space.

So critique an idea for me. Let me just throw a couple of random ones at you. So I’m a large company that has 10,000 employees and a long operating history. And what I want to do, and I’ve hired a bunch of people, and some people work out and some people don’t. And I have performance reviews that actually quantify how people are doing. And I say to you, here’s what I want to do: I want to take every resume that’s ever been submitted to us, [and] we’ve hired the person, and I want you to figure out – can you just help me predict the success of any given resume based on the all that hiring data and all that success or failure data?

Oh yeah, that’s a great one. We’ve never worked on a project exactly like that, we’ve done things that are pretty similar. So a problem like that, you’re dealing with a bunch of different kinds of data, you’re dealing with textual data; you may be dealing with categorical data, meaning information about where they graduated or what their degree was in; and you might even have numerical data, like, how long they work at a job or what their GPA was, things like that.

So a multi-modal problem like that is really ideal for a couple of different techniques. I would actually say that there’s a little bit of a secret in AI right now that a couple of techniques are dominating the field, and they’ve really boosted trees and deep learning models. And so trees are a little bit easier to work with initially, so I would take a stab at saying, “Let’s collect that data,” and let me back up and say that to solve a problem like this you would need a sufficient dataset, you would need thousands of instances of resumes and then the resulting hiring decision and performance information. But then you could quite readily build a system that would take in that text, take in the categorical data, take in the continuous numeric values, and essentially build a prediction system around those resumes of the predictive performance.